Information Centrality

Information Centrality is a network centrality metric that quantifies a node's importance based on the harmonic mean of the effective distances to all other nodes in the network, reflecting its capacity to efficiently acquire and disseminate information.

133

Publications

11.1K

Citations

327

Authors

228

Institutions

Publications per year

2017–2026

48

Variants

Current Flow Closeness Centrality

Authors

327

  1. NE
    Naomi Ehrich Leonard

    Princeton University, United States

    5 publications · 225 citations

    5

    h-index

  2. VL
    Vito Latora

    Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Italy

    4 publications · 938 citations

    4

    h-index

  3. MM
    Massimo Marchiori

    Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Italy

    3 publications · 741 citations

    3

    h-index

  4. YD
    Yong Deng

    University of Electronic Science and Technology of China, China

    3 publications · 253 citations

    3

    h-index

  5. JW
    Jianxin Wang

    Central South University, China

    3 publications · 443 citations

    3

    h-index

1–5 of 327

Venues

  1. 10

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 160 citations

    6

    h-index

  3. Social Networks

    6 publications · 4.5K citations

    6

    h-index

  4. SSRN Electronic Journal

    Repository · 5 publications · 247 citations

    5

    h-index

  5. Journal of Computational Science

    2 publications · 143 citations

    2

    h-index

1–5